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This paper introduces CAFD, a learning-based approach for DNN fault detection that integrates model-based, distance-based, and a novel concept-based feature called Concept Failure Ratio (CFR) derived from Vision-Language Models. CAFD consistently outperforms state-of-the-art baselines in fault detection rate across multiple datasets and budgets.